基于无模型深度强化学习的煤泥浮选智能控制研究
Research on intelligent control of coal slurry flotation based on model-free deep reinforcement learning
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摘要: 针对煤泥浮选工业现场缺乏有效的智能化控制方法问题，提出了一种基于改进深度强化学习的AS-DDPG控制方法。该方法在Actor-Critic网络的基础上引入注意力机制，能够精准提取时序数据中的关键特征，解决经典DDPG算法单独应用于时变对象的过程控制时面临收敛速度缓慢的问题。控制器设计方面，建立了包含矿浆浓度、灰分、流量等关键参数的多维状态空间，兼顾了尾煤质量与药剂回收率的多目标奖励函数，解决了传统基于机理模型智能控制存在的模型精度不足导致的控制难题。实验阶段利用现场数据驱动模型训练并实现策略迭代优化，仿真结果表明AS-DDPG算法较经典DDPG方法相比，训练误差降低了27%。现场工业性试验结果显示：灰分标准差降低了32.3%，捕收剂消耗降低了23.2%，起泡剂消耗降低了10.7%，表明本算法用于煤泥浮选智能控制的有效性，为煤泥浮选智能化提供了有益借鉴。Abstract: Aiming at the problem of lack of effective intelligent control methods in coal slurry flotation industrial site, an AS-DDPG control method based on improved deep reinforcement learning is proposed. The method introduces an attention mechanism on the basis of Actor-Critic network, which can accurately extract the key features in the temporal data and solve the problem of slow convergence speed faced by the classical DDPG algorithm when it is applied individually to the process control of time-varying objects. In terms of controller design, a multi-dimensional state space containing key parameters such as slurry concentration, ash, flow rate, etc., and a multi-objective reward function that takes into account the quality of tailings and the recovery rate of pharmaceuticals are established, which solves the control problems caused by the lack of model accuracy in traditional intelligent control based on the mechanistic model. The experimental phase uses field data to drive model training and achieve iterative strategy optimisation, and the simulation results show that the AS-DDPG algorithm reduces the training error by 27% compared with the classical DDPG method. The results of the field industrial test show that the standard deviation of ash is reduced by 32.3%, the consumption of trapping agent is reduced by 23.2%, and the consumption of frothing agent is reduced by 10.7%, which demonstrates the effectiveness of this algorithm for the intelligent control of coal slurry flotation, and provides a useful reference for the intelligence of coal slurry flotation.
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